Why I'm Not Sold On The Benefits of Being Overweight

I'm a bit late to this party, but I wanted to add my two cents to the debate that re-flared up a few weeks ago about the links between high BMI and mortality risk. The catalyst was this review in the Journal of the American Medical Association, which aggregated the results of studies with a total of 2.88 million subjects to conclude that (a) being "overweight" (BMI 25-30) reduces your risk of dying compared to "normal" BMI of 20-25, and (b) being class 1 obese (BMI 30-35) doesn't increase your risk relative to normal weight. Needless to say, this triggered the inevitable round of articles chastising health professionals for their "absurd fear of fat" and so on.

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(While I hate to spoil commenters' fun, I should point that everyone's risk of dying is 100 percent, regardless of BMI. I just can't be bothered to include the phase "during the study's follow-up period" nine times per paragraph!)

The truth is, these results are somewhat surprising, though similar results have been published several times over the past decade. And just because they're surprising doesn't mean they should be dismissed by those of us who are firmly convinced of the benefits of exercise and healthy diet. There are some very interesting points of discussion -- such as the possibility that, thanks to medical advances in areas like controlling high blood pressure and cholesterol, being obese may truly carry less of a health penalty than it used to. Moreover, I agree with those who argue that we should focus our attention on fitness, not fatness, as the best barometer of health. It's far better to be active, even if the scale tells you you're "overweight," than to be thin and sedentary.

Nonetheless, I'm not entirely willing to take the results at face value, for the reason shown in this graph:

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This shows data from a New England Journal of Medicine paper from two years ago. The paper is very, very similar to the recent JAMA paper (the graphs show hazard ratio, which is essentially your relative risk of dying, versus BMI), and indeed incorporate some of the same data. But they compared two different analyses: one for all 1.46 million subjects, and one just for subjects who had never smoked and showed no signs of cancer or heart disease when the study started.

What you see is that the curves have the same U-shape (being far too thin or far too fat is bad), but the "sweet spot" is slightly different: when you include smokers and people with serious illnesses, it appears that it's better to be a bit heavier. This effect has been discussed and debated for decades now, and it's pretty clear what's happening. Smoking is a classic confounding effect: it makes you thinner, and it kills you, which skews the data to suggest that being thin is bad -- even though it's the smoking, not the thinness, that is the problem. Same with serious illnesses like cancer, which often lead to weight loss many years before death (and often before they're even diagnosed).

Now, the authors of the recent JAMA paper aren't idiots. They know about smoking, and they've statistically "adjusted" the results to account for the effects of smoking. The problem is that statistical adjustment isn't a perfect process, especially when the factors are so tightly intertwined, as the NEJM paper points out:

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[S]moking is so strongly related to obesity and mortality [that it is] difficult to avoid residual confounding by means of typical adjustments for smoking status and number of cigarettes smoked per day.

In other words, you can't just wave a statistical wand and say "Presto! We've made the effects of smoking disappear through statistical adjustment." The only way to find out what happens in the absence of smoking is to conduct an analysis in people who've never smoked. That's what the graph above shows: when you look only at never-smokers, you get a significantly different picture, where having a BMI over 25 does incur a health penalty.

The debate between these two approaches can be summarized as generalizability versus validity. The fact is that many people in society smoke or have smoked in the past, and many people have serious conditions like cancer or heart disease -- so if you want data that you can accurately generalize to the population at large, you need to keep these people in the analysis. If you want data that's valid and not skewed by factors like smoking, the other side argues, you do have to exclude them.

There's merit on both sides. But from a purely selfish point of view, I'm interested in what the data says about my personal outlook -- and I've never smoked, and (touch wood) I don't currently have any signs of serious illness like cancer or heart disease. So in that case, my reading of the data is that the lowest-risk grouping for me is (as we've been told all along) a BMI between 20 and 25. More generally, this makes me think that there's still a good case to be made that -- in the absence of other factors -- being overweight is generally associated with higher relative health risk.

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(As in all discussions like this, there are lots of caveats to bear in mind. For example, BMI is a crude statistical tool that doesn't differentiate between being muscular and being fat. So no individual should treat BMI as the final arbiter of individual health -- it's just a tool that helps you understand what the big-picture patterns are.)

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